Linear regression for food groups consumption

Liner regression models were specified for each food group type.

\[ C_{i,j,k} = \alpha_{i,k} + \beta_{i,k} \times J\]

\(i=\{ \text{BMI categoried} \}\)

\(j=\{ \text{Age groups} \}\)

\(k=\{ \text{Gender} \}\)

Figures 1 through to 11 show the observed NPAS data for daily consumption of each food group, with the median and fitted linear regression.

The Intercept (\(\alpha_{i,k}\)) and change in consumption (\(\beta_{i,k}\)) are inputted for use in the SDM, presented in appendix.

Grains

Figure 1: Daily consumption of grains across age-groups, by gender and BMI

Vegetables

Figure 2: Daily consumption of vegetables across age-groups, by gender and BMI

Fruit

Figure 3: Daily consumption of fruits across age-groups, by gender and BMI

Meat and protein

Figure 4: Daily consumption of meat and protein across age-groups, by gender and BMI

Dairy

Figure 5: Daily consumption of diary across age-groups, by gender and BMI

Fats and oils

Figure 6: Daily consumption of fats and oils across age-groups, by gender and BMI

Discretionary foods

Figure 7: Daily consumption of discretionary foods across age-groups, by gender and BMI

Water

Figure 8: Daily consumption of water across age-groups, by gender and BMI

Sugar Sweetened beverage

Figure 9: Daily consumption of sugar sweetened beverages across age-groups, by gender and BMI

Other beverages (non-ssb, non-water)

Figure 10: Daily consumption of other beverages across age-groups, by gender and BMI

Other foods

Figure 11: Daily consumption of other foods across age-groups, by gender and BMI

Percetange macronutrients within each food groups

The aggregate definition of each food group has a variety of individual foods that differ in the composition of macronutrients. Figures 12 to 22 plot the percentage of the macronutrients used in the SDM by food group. The standardized estimates (ensure medians sum to 100) are presented with 95%CI. These percentages are a generalised simplification of each food group. The SDM inputs are presented in Table 1.

Grains

Figure 12: Composition of marconutrients for grains

Vegetables

Figure 13: Composition of marconutrients for vegetables

Fruit

Figure 14: Composition of marconutrients for fruit

Meat and protein

Figure 15: Composition of marconutrients for meat and protein

Dairy

Figure 16: Composition of marconutrients for dairy

Fats and oils

Figure 17: Composition of marconutrients for fats and ols

Discretionary foods

Figure 18: Composition of marconutrients for discretionary foods

Water

Figure 19: Composition of marconutrients for water

Sugar Sweetened Beverage

Figure 20: Composition of marconutrients for sugar sweetened beverage

Other beverages (non-ssb, non-water)

Figure 21: Composition of marconutrients for other beverages

Other foods

Figure 22: Composition of marconutrients for other foods

Linear regression for physical activity

Liner regression models were specified for each physical activity group

\[ D_{i,j,k} = \gamma_{i,k} + \omega_{i,k} \times J\]

\(i=\{ \text{BMI categoried} \}\)

\(j=\{ \text{Age groups} \}\)

\(k=\{ \text{Gender} \}\)

Figures 23 through to 28 show the observed NPAS data for daily duration of each activity group, with the median and fitted linear regression.

The Intercept (\(\gamma_{i,k}\)) and change in duration (\(\omega_{i,k}\)) are inputted for use in the SDM, presented in appendix.

Inactivity

Figure 23: Daily duration of inactivity across age-groups, by gender and BMI

Screen time

Figure 24: Daily duration of screen time across age-groups, by gender and BMI

Sleep time

Figure 25: Daily duration of sleep across age-groups, by gender and BMI

Light physical activity

Figure 26: Daily duration of light physical activity across age-groups, by gender and BMI

Moderate physical activity

Figure 27: Daily duration of moderate physical activity across age-groups, by gender and BMI

Vigorous physical activity

Figure 28: Daily duration of vigorous physical activity across age-groups, by gender and BMI

Linear regression coefficients for daily food groups consumption

Percentage of macronutrients by food item

median.summary.lcl.ucl %>% 
  select(food.groups, macro.groups, unweighted.median, unweighted.lcl, unweighted.ucl) %>% 
  mutate(food.groups = factor(case_when(food.groups=="Grains"~1,
                                 food.groups=="Vegetables"~2,
                                 food.groups=="Fruit"~3,
                                 food.groups=="Meat"~4,
                                 food.groups=="Dairy"~5,
                                 food.groups=="Fats"~6,
                                 food.groups=="Discretionary foods"~7,
                                 food.groups=="Water"~8,
                                 food.groups=="Sugar Sweetened beverage"~9,
                                 food.groups=="NonAlcoholic beverage"~10,
                                 food.groups=="Other"~11),
                              levels=c(seq(1:11)),
                              labels=c("Grains","Vegetables","Fruit","Meat and protein","Dairy",
                                       "Fats and oils","Discretionary foods","Water","Sugar Sweetened beverage",
                                       "Other beverages (non-ssb, non-water)","Other foods"))) %>%
  rename("Food groups"="food.groups") %>% 
  mutate(unweighted.median=sprintf("%0.2f", unweighted.median*100), 
         unweighted.lcl=sprintf("%0.2f", unweighted.lcl*100), 
         unweighted.ucl=sprintf("%0.2f", unweighted.ucl*100)) %>% 
  rename("Macronutrient"="macro.groups","Median"="unweighted.median", "LCL"="unweighted.lcl", "UCL"="unweighted.ucl") %>% 
  kable() %>% 
  add_header_above(., c(" ", " "," ","95% Confidence Intervals"=2)) %>%
  kable_styling(full_width = F)

Linear regression coefficients for daily physical activity duration

Definition of SDM food groups compared to NPAS (AUSNUT) food items

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